القائمة
FlowMaster backend: 29 deployed services, REST APIs, Event Bus patterns, MCP integration
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| name | flowmaster-backend |
| description | FlowMaster backend: 29 deployed services, REST APIs, Event Bus patterns, MCP integration |
| disable-model-invocation | false |
SNAPSHOT: 2026-02-08 11:00 Dubai Time (07:00 UTC) — likely changing soon
FlowMaster is a modular microservices platform with 29 services deployed and running on K3S (dev-02 server 65.21.52.58). All 79 requirements (R01-R79) have code deployed. ALL services are UNTESTED — no integration, health check, or e2e testing performed.
Each service owns its database and communicates via Event Bus (async) and REST APIs (sync).
Purpose: Design, version, and manage process definitions Tech: Python/FastAPI, ArangoDB Image: r03 | Reqs: R03-R20 Key Endpoints:
POST /processes — Create process definitionGET /processes/{id} — Retrieve processPUT /processes/{id} — Update processPOST /processes/{id}/publish — Publish versionGET /processes/{id}/versions — List versionsPurpose: Execute processes, orchestrate tasks, manage state Tech: Python/FastAPI, PostgreSQL + Redis Image: r21-r24 | Reqs: R21-R24 Key Endpoints:
POST /executions — Start executionGET /executions/{id} — Get execution statePUT /executions/{id}/pause — PausePUT /executions/{id}/resume — ResumeGET /executions/{id}/history — Event historyPurpose: Human-in-the-loop tasks for approvals, forms, decisions Tech: Python/FastAPI, ArangoDB + Redis Image: r24 | Reqs: R24 Key Endpoints:
POST /tasks — Create taskGET /tasks/{id} — Get task detailsPUT /tasks/{id}/assign — AssignPUT /tasks/{id}/respond — Submit responsePurpose: Central LLM/agent orchestrator with routing, caching, metering Tech: Python + Express.js, PostgreSQL + Redis + ArangoDB Image: latest Key Endpoints:
POST /agents/{id}/invoke — Call LLMPOST /agents/{id}/stream — Stream response
Port Assignment Note: Canonical registry assigns port 9007 (conflict resolution H1)Purpose: Document → structured data, embeddings, searchable content Tech: Python/FastAPI, ArangoDB Image: r01 | Reqs: R01-R02 Key Endpoints:
POST /documents/upload — Upload documentPOST /documents/{id}/extract — Extract entitiesPurpose: JWT/OAuth auth with RBAC/ABAC Tech: Python/FastAPI, PostgreSQL Image: ghcr.io latest Key Endpoints:
POST /auth/login — AuthenticatePOST /auth/refresh — Refresh tokenGET /auth/validate — Validate tokenPurpose: Single entrypoint, dynamic routing, rate limiting
Tech: Python/FastAPI (NOT Node.js), Redis + ArangoDB
Image: latest
Key Endpoints: * (dynamic proxy routing)
Purpose: Reliable event streaming with schema validation & replay Tech: Python/FastAPI + Kafka Image: latest Key Endpoints:
POST /events — Publish eventPOST /subscriptions — SubscribeGET /events/replay — Replay from timestampPurpose: Real-time communication, presence, streaming
Tech: Node.js/TypeScript, Socket.io + Redis
Image: 8ef0412c (4 restarts)
Key Endpoints: WS /connect, WS /channels/{id}/subscribe
Purpose: Email, SMS, push with templating & retry Tech: Python/FastAPI, PostgreSQL Image: r23-final Key Endpoints:
POST /notifications/send — Send notificationGET /notifications/{id} — Get statusPurpose: Cron triggers, time-based automation Tech: Python/FastAPI, PostgreSQL (APScheduler) Image: 192c806d Key Endpoints:
POST /schedules — Create schedulePOST /schedules/{id}/execute — Trigger nowPurpose: Service discovery and health monitoring Tech: Python/FastAPI Image: ghcr.io latest
Purpose: OAuth/SAML enterprise authentication Status: Working on dev-02 server (no dedicated repo)
Purpose: Process performance metrics, bottleneck detection Tech: Python/FastAPI Image: latest | Reqs: R48-R50 Key Endpoints:
GET /analytics/processes/{id} — Process metricsGET /analytics/dashboard — Overview dashboardPOST /analytics/reports — Generate reportPurpose: External system connectors, webhook management Tech: Python/FastAPI Image: r51-fix3 | Reqs: R51-R53 Key Endpoints:
POST /integrations — Register integrationPOST /integrations/{id}/execute — Execute connectorPOST /webhooks — Register webhook
Port Assignment Note: Canonical registry assigns port 9015 (conflict resolution H2)Purpose: Unified MCP gateway for AI agent access to FlowMaster + SDX Tech: Python/FastAPI Image: latest | Reqs: R54-R56 Key Endpoints: MCP protocol (tool listing, tool execution) Note: Shares port with API Gateway (9000) when proxied through nginx
Purpose: Organizational structure, legal entities, relationships Tech: Python/FastAPI Image: r66-fix | Reqs: R66-R68 Key Endpoints:
POST /entities — Create legal entityGET /entities/{id} — Get entityGET /entities/{id}/relationships — Get relationshipsPurpose: DMN-style decision tables, rule evaluation Tech: Python/FastAPI Image: r69-fix | Reqs: R69-R72 Key Endpoints:
POST /rules — Create rule setPOST /rules/{id}/evaluate — Evaluate rulesGET /rules/{id}/versions — Rule versionsPurpose: Process marketplace — publish, download, share processes Tech: Python/FastAPI Image: r73 | Reqs: R73-R76 Key Endpoints:
POST /marketplace/publish — Publish processGET /marketplace/search — Search marketplacePOST /marketplace/{id}/install — Install process
Port Assignment Note: Uses 9016; Agent Service uses 9016 (see port registry for clarification)Purpose: Agent personas, skills, learning from feedback Tech: Python/FastAPI Image: r36-r47 | Reqs: R36-R38, R45-R47 Key Endpoints:
POST /agents — Create agent personaPOST /agents/{id}/learn — Submit learning dataGET /agents/{id}/skills — Get agent skillsPurpose: Prompt templates, versioning, dynamic assembly Tech: Python/FastAPI Image: r39 | Reqs: R39-R41 Key Endpoints:
POST /prompts — Create templatePOST /prompts/{id}/render — Render with contextGET /prompts/{id}/versions — Template versionsPurpose: RAG, knowledge management, agent data access Tech: Python/FastAPI (revived from 38K lines internal-data-hub) Image: r42-r46 | Reqs: R42-R44, R46 Key Endpoints:
POST /knowledge/query — RAG queryPOST /knowledge/ingest — Ingest documentsPOST /api/v1/feedback — Feedback capture (R45)Purpose: Main admin UI, process management Tech: Next.js, React, TypeScript Image: ea2a29b
Purpose: Employee task execution with DXG + AI chat Tech: Next.js, React, TypeScript Image: r27-r45 | Reqs: R25-R29, R45 Key Features: DXG briefing, analytics widgets, AI chat, feedback capture
Purpose: Agent escalation handling dashboard Tech: Next.js, React, TypeScript Image: r30 | Reqs: R30-R33 Port Assignment Note: Canonical registry assigns port 3005 (conflict resolution H4)
Purpose: Visio-quality drag-and-drop BPMN editor Tech: Next.js, React, TypeScript, ReactFlow Image: r77 | Reqs: R77-R79 Key Features: Swimlane views, inline AI assistant, drag-and-drop
Purpose: Dynamic Experience Generator — AI-powered UI from workflow context Tech: Python/FastAPI Image: r34-r35 | Reqs: R34-R35 Key Endpoints:
POST /api/v1/analyze/{task_id} — Context analysisPOST /api/v1/smart-form/{task_id} — Pre-filled formPOST /api/v1/briefing/{task_id} — Case summaryPurpose: Flow chart views, process visualization Tech: Python/FastAPI Image: r57-fix | Reqs: R57-R59
Purpose: Process version management, diff, branching Tech: Python/FastAPI Image: r60-fix | Reqs: R60-R62
Purpose: Cross-process dependencies and linking Tech: Python/FastAPI Image: r63 | Reqs: R63-R65
All API calls must include:
Authorization: Bearer {accessToken}
X-Tenant-Id: {tenantId}
X-Service-Name: {serviceName} (service-to-service)
src/
├── api/ # HTTP controllers
├── core/ # Business logic
├── domain/ # Entity models
├── infra/
│ ├── db/ # Database repository
│ ├── messaging/ # Event Bus integration
│ ├── cache/ # Redis adapter
│ └── http/ # External service clients
├── config/ # Environment & DI
├── security/ # Auth/RBAC
└── utils/ # Helpers
What was done:
What was NOT done:
flow-master group, private)HCB-Consulting-ME org)IMPORTANT: All port assignments have been canonicalized in PORT_REGISTRY.md to resolve conflicts:
| Issue | Resolution | Status |
|---|---|---|
| H1: Port 9006 | Human Task Service (9006) + AI Agent Service (9007) | ✅ Resolved |
| H2: Port 9014 | Process Analytics (9014) + External Integration (9015) | ✅ Resolved |
| H3: Port 9000 | API Gateway (9000) canonical assignment | ✅ Resolved |
| H4: Port 3001 | Engage App (3001) + Manager App (3005) + Designer (3002) | ✅ Resolved |
Core Services:
9000 - API Gateway (entrypoint)9002 - Document Intelligence9003 - Process Design9005 - Execution Engine9006 - Human Task Service9007 - AI Agent Service (NEW ASSIGNMENT)9008 - Scheduling9009 - Notifications9010 - WebSocket Gateway9011 - DXG Service9013 - Event Bus9014 - Process Analytics9015 - External Integration (NEW ASSIGNMENT)9016 - Agent ServiceAuthentication & Infrastructure:
8001 - Service Registry8002 - Authentication8009 - Knowledge Hub8014 - Legal Entity Service8018 - Business Rules Engine8019 - Process Views8020 - Process Versioning8021 - Process LinkingFrontends:
3000 - Main Frontend3001 - Engage App3002 - Process Designer3005 - Manager AppSee PORT_REGISTRY.md for complete details
Load CI/CD pipeline configuration and deployment information when working with automation or deployments
Load database relationships, shared resources, and schema information when working with data models or database configuration
Load development environment information including folder structure, OrbStack setup, and system configuration
Load port mappings for all projects when working with networking, docker-compose, or service configuration
Load GitHub/GitLab repository information when working with git, CI/CD, or repository management
Load server information, infrastructure details, and access patterns when working with deployment or server configuration